In existing computer-related image segmentation approaches, the boundaries between objects, often, are not clearly defined, or are arbitrary. Additionally, algorithms associated with such existing approaches that attempt to segment a scene into meaningful and/or salient regions commonly lack an ability to determine a unifying and general metric for visual similarity that reliably groups sub-regions of an object into a single object.
In some such approaches, hierarchical segmentation algorithms attempt to create a hierarchy of segmentations at each of multiple levels by changing the criteria for clustering to be most particular at the leaf descendent levels and most inclusive at the higher parent ancestor levels. However, such algorithms commonly result in an increase in the number of irrelevant, incomplete or incorrect segmentations.
Accordingly, a need exists for improved techniques for segmenting objects in static images.